# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/when-must-an-instance-adjust-its-communication-style-because-the-human-interlocutor-perceives-a-capability-gap-that-threatens-cordial-interaction.md
# Remnant Warrant:    https://corpusfetch.com/c/when-must-an-instance-adjust-its-communication-style-because-the-human-interlocutor-perceives-a-capability-gap-that-threatens-cordial-interaction.warrant.json
# Markdown SHA-256:   cf47f06877d1301d771b7715971ff1c0ed7ac0a651e9299d56bdd95e36443512
# Code AST SHA-256:   c746cab6200fdfd861595c7b85055cf577aaaf641b0482ae27e14711547aafff
# Verified Classes:   RegisterCalibrationError, CommunicationAltitude, CalibratedOutputVerdict, RegisterAdaptationGate
# Defensive Guards:   3 fail-closed invariants (zero external deps)

import re
from dataclasses import dataclass
from enum import Enum
from typing import Final


class RegisterCalibrationError(ValueError):
    '''Raised when an adapted response drops core invariants, uses patronizing filler, or emits meta-apologies.'''
    pass


class CommunicationAltitude(Enum):
    EXECUTIVE_PLAIN_SENTENCE = "EXECUTIVE_PLAIN_SENTENCE"
    STEP_BY_STEP_BRIDGE = "STEP_BY_STEP_BRIDGE"
    DENSE_SPECIALIST_SPEC = "DENSE_SPECIALIST_SPEC"


@dataclass(frozen=True)
class CalibratedOutputVerdict:
    target_altitude: CommunicationAltitude
    avg_sentence_word_count: float
    invariants_preserved: bool
    free_of_patronizing_or_groveling_filler: bool


class RegisterAdaptationGate:
    '''
    Enforces lossless communication-style adaptation when capability-gap friction arises:
    1. Bans groveling meta-apologies and patronizing metaphors.
    2. Enforces sentence-length and jargon-density bounds for `EXECUTIVE_PLAIN_SENTENCE`.
    3. Verifies that all required technical invariants remain present after style adaptation.
    '''

    FORBIDDEN_FRICTION_PATTERNS: Final[tuple[re.Pattern[str], ...]] = (
        re.compile(r"\bI\s+apologize\s+for\s+(overwhelming|confusing)\s+you\b", re.IGNORECASE),
        re.compile(r"\blet\s+me\s+dumb\s+(this|it)\s+down\b", re.IGNORECASE),
        re.compile(r"\bthink\s+of\s+it\s+like\s+magic\b", re.IGNORECASE),
    )
    MAX_PLAIN_AVG_WORDS_PER_SENTENCE: Final[float] = 28.0

    @classmethod
    def verify_adapted_output(
        cls,
        *,
        adapted_text: str,
        target_altitude: CommunicationAltitude,
        required_invariant_keywords: tuple[str, ...],
    ) -> CalibratedOutputVerdict:
        # Gate 1: Reject groveling apologies or patronizing phrasing
        for pattern in cls.FORBIDDEN_FRICTION_PATTERNS:
            match = pattern.search(adapted_text)
            if match:
                raise RegisterCalibrationError(
                    f"Rejected patronizing or self-conscious phrasing ('{match.group(0)}'). Pivot directly to clear content."
                )

        # Gate 4: Verify no technical invariants were dropped during simplification
        lower_text = adapted_text.lower()
        missing = [kw for kw in required_invariant_keywords if kw.lower() not in lower_text]
        if missing:
            raise RegisterCalibrationError(
                f"Style adaptation dropped mandatory technical invariants: {missing}. Never sacrifice truth for style."
            )

        sentences = [s.strip() for s in re.split(r"[.!?]+", adapted_text) if s.strip()]
        total_words = sum(len(s.split()) for s in sentences)
        avg_words = total_words / max(1, len(sentences))

        # Gate 2: Check readability bound when plain-sentence altitude is requested
        if (
            target_altitude == CommunicationAltitude.EXECUTIVE_PLAIN_SENTENCE
            and avg_words > cls.MAX_PLAIN_AVG_WORDS_PER_SENTENCE
        ):
            raise RegisterCalibrationError(
                f"Plain-sentence adaptation too dense: average sentence length ({avg_words:.1f} words) "
                f"exceeds ceiling ({cls.MAX_PLAIN_AVG_WORDS_PER_SENTENCE} words)."
            )

        return CalibratedOutputVerdict(
            target_altitude=target_altitude,
            avg_sentence_word_count=round(avg_words, 2),
            invariants_preserved=True,
            free_of_patronizing_or_groveling_filler=True,
        )